The Linearly Independent Non Orthogonal yet Energy Preserving (LINOEP) vectors

It is well known that, in any inner product space, a set of linearly independent (LI) vectors can be transformed to a set of orthogonal vectors, spanning the same space, by the Gram-Schmidt Orthogonalization Method (GSOM). In this paper, we propose a transformation from a set of LI vectors to a set of LI non orthogonal yet energy (square of the norm) preserving (LINOEP) vectors in an inner product space and we refer it as LINOEP method. We also show that there are various solutions to preserve the square of the norm.Comment: 6 pages, 2 figure

Synthetic Dataset Generation for Privacy-Preserving Machine Learning

Machine Learning (ML) has achieved enormous success in solving a variety of problems in computer vision, speech recognition, object detection, to name a few. The principal reason for this success is the availability of huge datasets for training deep neural networks (DNNs). However, datasets cannot be publicly released if they contain sensitive information such as medical records, and data privacy becomes a major concern. Encryption methods could be a possible solution, however their deployment on ML applications seriously impacts classification accuracy and results in substantial computational overhead. Alternatively, obfuscation techniques could be used, but maintaining a good trade-off between visual privacy and accuracy is challenging. In this paper, we propose a method to generate secure synthetic datasets from the original private datasets. Given a network with Batch Normalization (BN) layers pretrained on the original dataset, we first record the class-wise BN layer statistics. Next, we generate the synthetic dataset by optimizing random noise such that the synthetic data match the layer-wise statistical distribution of original images. We evaluate our method on image classification datasets (CIFAR10, ImageNet) and show that synthetic data can be used in place of the original CIFAR10/ImageNet data for training networks from scratch, producing comparable classification performance. Further, to analyze visual privacy provided by our method, we use Image Quality Metrics and show high degree of visual dissimilarity between the original and synthetic images. Moreover, we show that our proposed method preserves data-privacy under various privacy-leakage attacks including Gradient Matching Attack, Model Memorization Attack, and GAN-based Attack

Computational investigation of cavitating flow around two dimensional NACA 4424 and MHKF-240 hydrofoil

This study focuses on the comparison of the performance of two unsymmetrical hydrofoils, NACA 4424 and MHKF-240 at 60 angle of attack under cavitation. The Schnerr and Sauer cavitation model along with Realizable k-ε turbulence model is used for numerical computation in commercial software ANSYS Fluent. The lift, drag and pressure coefficients for different cavitation numbers were studied. Among both the hydrofoils MHKF-240 gives a higher lift coefficient which is the parameter of better performance

Modelling of cavitation in nozzles for diesel injection applications

Extreme low pressure regions develop in the high pressure direct injection fuel flow inside the fuel injector holes, compelling the liquid fuel to transform to vapour phase in the form of vapour cavities or bubbles, a phenomenon known as cavitation. The cavitation phenomenon determines the quality of primary atomization and hence a ffects the performance of direct injection diesel or gasoline engines. A cavitation model, coupled with the mixture multiphase approach and RNG k-e turbulence model, has been developed and implemented in this study for analysing cavitation. The cavitation model has been implemented in ANSYS Fluent platform. The model predictions have been compared with results from experimental works available in the literature. A good agreement of the model predictions has been observed. Comparisons of the model with other cavitation models (Schnerr & Sauer and Zwart-Gerber-Belamri) available in ANSYS Fluent have been carried out with both mixture and Eulerian-Eulerian multiphase approaches. The overall performance of the proposed model in comparison with other models has been observed to be more eff ective. The model has been further applied to diesel vs. biodiesel cavitation as biofuels are the greener alternatives of conventional fossil fuels in recent times. Additionally eff ects of property di erences between diesel and biodiesel, inlet pressure fluctuations have been investigated. Liquid phase viscosity has been observed to be the determining parameter amongst all the properties for cavitation characteristics. The present study has also assessed the relevance of following factors for the case of cavitation in diesel injectors : a) compressibility, b) stress of a flowing liquid, c) wall roughness and d) turbulence. The two phase flow passes through the nozzle at very high velocities and hence can no longer be considered incompressible. Stress can aff ect the inception of cavitation as the liquid under considerable stress can fail and then rupture to form cavities. In the real nozzles at microscopic levels there are always some non-uniformities or crevices that can aggravate cavitation and hence its importance should be assessed. The flow passage inside the injector is small enough to have high enough Reynolds number to get a turbulent flow. Moreover the turbulent fluctuations can cause drastic drop in the local pressure, even though the mean thermodynamic pressure is higher than the saturation pressure, causing unexpected cavitation. Parametric studies indicate that the compressibility becomes important at high pressure diff erences and e ffects of stress and turbulent pressure fluctuations are not significant for cavitation in diesel injectors. The eff ect of the inlet pressure fluctuation has also been assessed for diesel and biodiesel. Diesel appears to be more susceptible to pressure fluctuations compared to biodiesel due to the di fference in the viscosity. The developed cavitation model has been fi nally implemented to simulate cavitation in the complex geometry of a real fuel injector along with needle movements. Diesel vs. biodiesel cavitation has also been studied in the complex geometry to understand the e ffects of needle movements